# One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark

arXiv: https://arxiv.org/abs/2610.04155 · HTML: https://mlsanity.com/paper/2610.04155/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 8 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 8 of 8)

- [A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management](https://mlsanity.com/paper/2509.09053/) (2025)
- [Robustness Evaluation of Machine Learning Models for Fault Classification and Localization In Power System Protection](https://mlsanity.com/paper/2512.15385/) (2025)
- [Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection](https://mlsanity.com/paper/2510.00831/) (2025)
- [PROTECT-90: A Fault Dataset for Power System Protection](https://mlsanity.com/paper/2606.24298/) (2026)
- [Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids](https://mlsanity.com/paper/2605.17256/) (2026)
- [A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids](https://mlsanity.com/paper/2609.16744/) (2026)
- [Transformer is All You Need: Attention-Based Anomaly Detection and Classification in Inverter-Rich Power Systems](https://mlsanity.com/paper/2607.13537/) (2026)
- [The Wisdom of the Crowd: High-Fidelity Classification of Cyber-Attacks and Faults in Power Systems Using Ensemble and Machine Learning](https://mlsanity.com/paper/2511.06714/) (2025)

## Abstract (arXiv)

Open electromagnetic-transient datasets are beginning to make reproducible learning-based protection studies possible, but the practical use of these datasets still requires application-level benchmarks that define timing, sensing, and validation assumptions. This paper presents an initial application benchmark on the recently released PROTECT-90 dataset for two protection-oriented tasks: fault-type classification and discrete faulted-line identification. A compact one-dimensional convolutional neural network (CNN) is evaluated using post-inception windows of 0.25, 0.5, 1, and 2 cycles under strict episode-wise splitting. A non-convolutional multilayer perceptron (MLP) is also trained as an architecture-control baseline. The results show that both tasks are nearly saturated under full observability, with one-cycle test accuracies of 99.84% for fault type and 100.00% for line identification. The main performance variation appears under reduced observability: current-only inputs preserve line identification accuracy at 100.00%, whereas voltage-only inputs reduce line identification accuracy to 53.09% with the CNN and 50.57% with the MLP. This indicates that the limiting factor is measurement information rather than neural architecture. Additional stratified checks show stable performance across topology states and fault-resistance bins, while CPU inference contributes only 0.528 ms to the one-cycle total decision time of 20.53 ms.